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Funding Trends Discussion Series

Overview

The MIDAS Funding Trends Discussion Series invites all U-M faculty and researchers to engage in discussion on the evolving landscape of data science and AI research funding, featuring presentations from experts in the field. Attendees can expect to gain perspective on the current trends and opportunities in research funding for data science across a variety of fields. These seminars also include ample time for attendees to network and build collaboration with their colleagues across campus. 

Please note that these seminars are limited to in-person attendance; we are not able to provide live-streaming. Also please be aware that event photography will be in use. Attendees who do not wish to release their photos are asked to pick up a red lanyard at check-in.  

For more information, or if you are interested in presenting at this series, please contact the MIDAS Research Manager, Beth Uberseder (ubersbe@umich.edu)

Upcoming Events

 More dates and speakers TBA

Past Speakers

Sept.12, 2023

NSF Priorities and Opportunities for Data Science and AI with Pamela Davis-Kean

About: As data science and AI have continued to permeate society, the National Science Foundation (NSF) has introduced new programs in the foundations of the data sciences and its applications to scientific problems. This talk provided an overview of some of NSF’s activities and opportunities in data science and related fields, with a particular focus on the Social, Behavioral, and Economic (SBE) Directorate.

Dr. Pamela Davis-Kean, Former Program Director in the Developmental Science Program of the NSF SBE Directorate and the Developmental Area Chair and Professor in the University of Michigan Department of Psychology, and Research Professor at the Institute for Social Research

Biography

Dr. Pamela Davis-Kean is the Developmental Area Chair and Professor in the University of Michigan Department of Psychology as well as a Research Professor at the Institute for Social Research where she is the Founding Director of the Population, Neurodevelopment, and Genetics (PNG) program. Dr. Davis-Kean was an Associate Director of the Michigan Institute for Data Science, and recently served as the Program Director in the Developmental Science Program of the Social, Behavioral, and Economic (SBE) Directorate of the National Science Foundation.

 

Dr. Davis-Kean’s research focuses on the various pathways that the socio-economic status (SES) of parents relates to the cognitive/achievement outcomes (particularly mathematics) of their children. Her primary focus is on parental educational attainment and how it can influence the development of the home environment throughout childhood, adolescence, and the transition to adulthood. Davis-Kean is also a Research Professor at the Institute for Social Research where she is the Founding Director of the Population, Neurodevelopment, and Genetics (PNG) program. This collaboration examines the complex transactions of the brain, biology, and behavior as children and families develop across time and is representative of the population. She has expertise in quantitative methods, data science, the use of secondary data for longitudinal analyses, and open science practices. Her current research is converging methods in social science and computer science to examine the flow of parenting information on social media platforms.

June 1, 2023

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Dr. Alfred Hero, Program Director in the NSF CISE Directorate, and the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and R. Jamison and Betty Williams Professor of Engineering, University of Michigan.

Biography

Alfred O. Hero, PhD, is the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan and co-Director of the Michigan Institute for Data Science.
The Hero group focuses on building foundational theory and methodology for data science and engineering. The Hero group is developing theory and algorithms for data collection, analysis and visualization that use statistical machine learning and distributed optimization. These are being to applied to network data analysis, personalized health, multi-modality information fusion, data-driven physical simulation, materials science, dynamic social media, and database indexing and retrieval.